An efficient cooling system for data centers can boost the working efficiency of servers and promote energy savings. In this study, a laboratory experiment and computational fluid dynamics (CFD) simulation were performed to explore the performance of a two-phase cooling system. The coefficient of performance (COP) and partial power usage effectiveness (pPUE) of the proposed system was evaluated under various IT (Information Technology) loads. The relationship between the interval of the two submerged servers and their surface temperatures was evaluated by CFD analysis, and the minimum intervals that could maintain the temperature of the server surfaces below 85 °C were obtained. Experimental results show that as server power increases, COP increases pPUE decreases. In one experiment, the COP increased from 19.0 to 26.7, whereas pPUE decreased from 1.053 to 1.037. The exergy efficiency of this system ranges from 12.65% to 18.96%, and the tank side accounts for most of the exergy destruction. The minimum intervals between servers are 15 mm under 1000 W of power, 20 mm under 1500 W, and more than 30 mm under 2000 W and above. The observations and conclusions in this study can be valuable references for the study of cooling systems in data centers.

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model.

The analysis of energy configuration in the planning of data-center-park-integrated energy systems (DCP-IESs) has become an enormous challenge, owing to multi-energy complementarity, energy cascade use, and energy security. In this study, a configuration model of DCP-IESs was established to obtain the economic and low-carbon energy uses of the data centers, based on mixed integer linear programming. In the model, carbon emissions were converted to economic indicators through carbon pricing. Then, the configuration model was modified according to the security of the proposed device switching logic, and the Markov-based reliability estimation method was used to ensure the redundant design of the configuration. Using the new energy configuration method, the DCP-IES configuration scheme could be obtained under economical, low-carbon, and high reliability conditions. A data center park in Shanghai was selected as a case study, and the results are as follows: it will only take 2.88 years for the economics of DCP-IES to reach those of traditional data center energy systems. Additionally, the use of configuration model in DCP-IES would result in a reduction in annual carbon emissions of 39,323 tons, with a power usage effectiveness of 1.388, whereas an increase in reliability results in an increasingly faster increase in the initial investment cost.

Cooling systems can effectively enable information and communication technology (ICT) equipment to utilize power more efficiently in data centers (DCs). Two-phase cooling provides a potential method to cool CPUs and other electronics and involves submerging them in a thermal conductive dielectric liquid or coolant. In this study, an innovative cooling structure and a two-phase immersion cooling system procedure are presented. The CPU is directly submerged in an engineered fluid, and an experimental test is conducted to obtain the boiling heat transfer and energy consumption data. A prediction equation for saturated boiling HTC in the pool, based on the influence of the characteristics of the heat transfer surface of the CPU and the physical parameters, is proposed. The average partial power usage effectiveness (pPUE) value of the proposed system is 1.036, indicating a significantly improved energy conservation effect compared to conventional air cooling systems. Studies have shown that direct-touch heat dissipation is suitable for supercomputer server CPU heat dissipation with low heat flux density, while for high-density CPU heat dissipation, it is easy to reach the maximum temperature limit of the CPU, thereby reducing the CPU frequency.